A common strategy for classifying hand movements with DNN-based algorithms is converting sEMG signals into images and then using Convolutional Neural Networks (CNNs) to detect hand movements [1]}, [2]}, [3]}, [4]}. sEMG signals are, however, sequential in nature, and CNNs cannot extract temporal features. In this regard, recent literature [5]}, [6]} used recurrent architectures such as Long Short Term Memory (LSTM) networks to consider the sequential nature of sEMG signals. In addition, LSTMs and CNNs can be combined as a hybrid architecture [7]} to jointly extract the temporal and spatial properties of sEMG signals. Although sequence modeling with recurrent networks is a common approach, it can have disadvantages, such as lack of parallelism during training, exploding/vanishing gradient, and extensive memory/computation requirements [8]}. Therefore, Reference [8]} proposed \(\text{Temporal Convolutions}\)  (TCs) for extracting temporal information in time series tasks while addressing the aforementioned challenges of the recurrent architectures. In addition Reference [6]} proposed the concept of temporal dilation of LSTM to reduce the computational cost, memory, requirement, and model complexity. On the other hand, attention-based architectures such as Transformers [11]}, [12]} show great potentials for widespread adoption in different Artificial Intelligence (AI) applications. In particular, the transformer-based architectures could improve the recognition accuracy compared to their state-of-the-art counterparts (where LSTM or hybrid LSTM-CNN are adopted). However, transformers are limited by the memory and computation requirements of the quadratic operation in attention for long sequences or images. Therefore, in [12]}, the authors proposed dividing the images into patches and then using the flattened patches as the input for the transformers. Inspired by the progress of attention mechanism and TCs, recent literature [14]}, [15]}, [16]} proposed a Few-shot learning architecture, which is based on the combination of attention and temporal convolutions.
